SoroushVahidi/scidocs huggingface dataset:
Noise Modeling for RF CMOS Circuit Simulation
(2001)

The RF noise in 0.18m CMOS technology has been measured and modeled. In contrast to some other groups, we find only a moderate enhancement of the drain current noise for shortchannel MOSFETs. The gate current noise on the other hand is more significantly enhanced, which is explained by the effects of the gate resistance. The experimental results are modeled with a nonquasi-static RF model, based on channel segmentation, which is capable of predicting both drain and gate current noise accurately. Experimental evidence is shown for two additional noise mechanisms: 1) avalanche noise associated with the avalanche current from drain to bulk and 2) shot noise in the direct-tunneling gate leakage current. Additionally, we show low-frequency noise measurements, which strongly point toward an explanation of the1 noise based on carrier trapping, not only in n-channel MOSFETs, but also in p-channel MOSFETs.
Topology Aware Fully Convolutional Networks for Histology Gland Segmentation
(2016)

[1] Sirinukunwattana et al. Gland Segmentation in colon histology images: the GlaS contest, arXiv 2016 [2] Long et al. Fully convolutional networks for semantic segmentation, CVPR 2015 [3] Chend et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs, arXiv 2014 [4] Zheng et al. Conditional random fields as recurrent neural networks, ICCV 2015 Alexnet-FCN (6 layers) FCN-8s (16 layers) UNet (23 layers) DN (26 layers) 0.93 0.79 0.86 0.83
Learning to predict eye fixations for semantic contents using multi-layer sparse network
(2014)

In this paper, we present a novel model for saliency prediction under a unified framework of feature integration. The model distinguishes itself by directly learning from natural images and automatically incorporating higher-level semantic information in a scalable manner for gaze prediction. Unlike most existing saliency models that rely on specific features or object detectors, our model learns multiple stages of features that mimic the hierarchical organization of the ventral stream in the visual cortex and integrate them by adapting their weights based on the ground-truth fixation data. To accomplish this, we utilize a multi-layer sparse network to learn low-, midand high-level features from natural images and train a linear support vector machine (SVM) for weight adaption and feature integration. Experimental results show that our model could learn high-level semantic features like faces and texts and can perform competitively among existing approaches in predicting eye fixations. & 2014 Elsevier B.V. All rights reserved.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
(2018)

In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.
A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models
(2013)

The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content-based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy-or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.
Collaborative color calibration for multi-camera systems
(2011)

This paper proposes a collaborative color calibration meth od for multi-camera systems. The multi-camera color calibr t on problem is formulated as an overdetermined linear system, i n which a dynamic range shaping is incorporated to ensure the high contrasts for captured images. The cameras are calibrated w ith the parameters obtained by solving the linear system. Fo r non-planar multi-camera systems, we design a novel omnidirectional co lor checker and present a method for establishing global cor respondences to facilitate automatic color calibration without m anual adjustment. According to experimental results on bot h synthetic and real-system datasets, the proposed method shows high perfo rmance in achieving inter-camera color consistency and hig h dynamic range. Thanks to the generality of the linear system formula tion and the flexibility of the designed color checker, the pr oposed method is applicable to various multi-camera systems.
Dynamic Reserve Prices for Repeated Auctions: Learning from Bids - Working Paper
(2014)

A large fraction of online advertisements are sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations and competition among the bidders, previous bids can be used to learn the distribution of the valuations and to update the reserve prices. We present a simple approximately incentive-compatible and optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve in such settings.
DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field
(2016)

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).
CDnet 2014: An Expanded Change Detection Benchmark Dataset
(2014)

Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.
Does Online Technology Make Us More or Less Sociable? A Preliminary Review and Call for Research.
(2018)

How does online technology affect sociability? Emerging evidence-much of it inconclusive-suggests a nuanced relationship between use of online technology (the Internet, social media, and virtual reality) and sociability (emotion recognition, empathy, perspective taking, and emotional intelligence). Although online technology can facilitate purely positive behavior (e.g., charitable giving) or purely negative behavior (e.g., cyberbullying), it appears to affect sociability in three ways, depending on whether it allows a deeper understanding of people's thoughts and feelings: (a) It benefits sociability when it complements already-deep offline engagement with others, (b) it impairs sociability when it supplants deeper offline engagement for superficial online engagement, and (c) it enhances sociability when deep offline engagement is otherwise difficult to attain. We suggest potential implications and moderators of technology's effects on sociability and call for additional causal research.
A Rule-Based Approach For Effective Sentiment Analysis
(2012)

The success of Web 2.0 applications has made online social media websites tremendous assets for supporting critical business intelligence applications. The knowledge gained from social media can potentially lead to the development of novel services that are better tailored to users’ needs and at the same time meet the objectives of businesses offering them. Online consumer reviews are one of the critical social media contents. Proper analysis of consumer reviews not only provides valuable information to facilitate the purchase decisions of customers but also helps merchants or product manufacturers better understand general responses of customers on their products for marketing campaign improvement. This study aims at designing an approach for supporting the effective analysis of the huge volume of online consumer reviews and, at the same time, settling the major limitations of existing approaches. Specifically, the proposed rule-based sentiment analysis (R-SA) technique employs the class association rule mining algorithm to automatically discover interesting and effective rules capable of extracting product features or opinion sentences for a specific product feature interested. According to our preliminary evaluation results, the R-SA technique performs well in comparison with its benchmark technique.
Making Sense of Entities and Quantities in Web Tables
(2016)

HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad-hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re-use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables.
 This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base.
 To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption), monetary (e.g. revenue), temporal (e.g. date) and dimensionless (e.g. counts) measures.
 Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.
A Laplacian Framework for Option Discovery in Reinforcement Learning
(2017)

Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents’ intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.
Accessible Crowdwork?: Understanding the Value in and Challenge of Microtask Employment for People with Disabilities
(2015)

We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.
Breakfast reduces declines in attention and memory over the morning in schoolchildren
(2003)

Twenty-nine schoolchildren were tested throughout the morning on 4 successive days, having a different breakfast each day (either of the cereals Cheerios or Shreddies, glucose drink or No breakfast). A series of computerised tests of attention, working memory and episodic secondary memory was conducted prior to breakfast and again 30, 90, 150 and 210 min later. The glucose drink and No breakfast conditions were followed by declines in attention and memory, but the declines were significantly reduced in the two cereal conditions. This study provides objective evidence that a typical breakfast of cereal rich in complex carbohydrates can help maintain mental performance over the morning.
Customer relationship management capabilities Measurement , antecedents and consequences
(2012)

Purpose – This study seeks to extend the resource-based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities. Design/methodology/approach – Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling. Findings – A three-factor (customer interaction management capability, customer relationship upgrading capability and customer win-back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer-centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on organizational performance. Practical implications – This study provides a useful measurement mode of CRM capabilities that managers can use to evaluate the status in quo of CRM capabilities of their firms. Managers may also improve their CRM programs more effectively and efficiently by deploying such strategic resources of firms as customer orientation, customer-centric organizational system and CRM technology to build and strengthen their CRM capabilities. Originality/value – The paper addresses significant gaps in the current literature by taking a capability view of CRM, developing a valid measurement model of CRM capabilities, and examining how possession of important CRM resources influences the effective deployment of CRM capabilities.
Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume
(2018)

Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.
Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform
(2016)

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.
Mobile technology and the digitization of healthcare.
(2016)

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.
Experimental implementation of underactuated potential energy shaping on a powered ankle-foot orthosis
(2016)

Traditional control methodologies of rehabilitation orthoses/exoskeletons aim at replicating normal kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. An alternative control category, kinetic control, enforces kinetic goals (e.g., torques or energy) instead of kinematic trajectories, which could provide a flexible learning environment for the user while freeing up therapists to make corrections. We propose that the theory of underactuated potential energy shaping, which falls into the category of kinetic control, could be used to generate virtual body-weight support for stroke gait rehabilitation. After deriving the nonlinear control law and simulating it on a human-like biped model, we implemented this controller on a powered ankle-foot orthosis that was designed specifically for testing torque control strategies. Experimental results with an able-bodied human subject demonstrate the feasibility of the control approach for both positive and negative virtual body-weight augmentation.